Plot positions¶

direct link

In [1]:
import pandas as pd
In [2]:
# parameters

combined_data_file = "combined_positions.csv"
heincke_positions = "heincke.csv"

platform_deploy_times = {
    "Littorina": "2023-04-24T05:30:00",
    "D298": "2023-04-24T08:50:00",
    "D299": "2023-04-24T08:50:00",
    "D300": "2023-04-24T08:50:00",
    "D301": "2023-04-24T09:35:00",
    "D302": "2023-04-24T09:35:00",
    "D303": "2023-04-24T09:35:00",
}
In [3]:
import pandas as pd
import hvplot.pandas
from bokeh.models import HoverTool, SaveTool
import geoviews as gv

from functools import reduce

Read data¶

In [4]:
df = pd.read_csv(combined_data_file, parse_dates=["Timestamp", ])
df
Out[4]:
Timestamp Longitude Latitude Longitude_ Latitude_ Platform Type
0 2023-04-24 00:55:07+00:00 10.18159 54.32806 10.20000 54.30000 Littorina Vessel
1 2023-04-24 01:55:10+00:00 10.18163 54.32806 10.20000 54.30000 Littorina Vessel
2 2023-04-24 02:55:14+00:00 10.18160 54.32805 10.20000 54.30000 Littorina Vessel
3 2023-04-24 03:58:09+00:00 10.18160 54.32809 10.20000 54.30000 Littorina Vessel
4 2023-04-24 04:58:10+00:00 10.18161 54.32804 10.20000 54.30000 Littorina Vessel
... ... ... ... ... ... ... ...
13749 2023-05-09 08:40:47+00:00 10.73319 54.93622 10.73319 54.93622 D303 Buoy
13750 2023-05-09 08:45:46+00:00 10.73306 54.93580 10.73306 54.93580 D303 Buoy
13751 2023-05-09 09:20:47+00:00 10.73313 54.93616 10.73313 54.93616 D303 Buoy
13752 2023-05-09 09:25:48+00:00 10.73306 54.93605 10.73306 54.93605 D303 Buoy
13753 2023-05-09 09:40:44+00:00 10.73317 54.93613 10.73317 54.93613 D303 Buoy

13754 rows × 7 columns

In [5]:
df_heincke = pd.read_csv(heincke_positions, parse_dates=["Timestamp", ])
df_heincke["Latitude_"] = df_heincke.Latitude
df_heincke["Longitude_"] = df_heincke.Longitude
df_heincke
Out[5]:
Timestamp Longitude Latitude Platform Type Latitude_ Longitude_
0 2023-05-08 12:58:00 8.58042 53.52311 Heincke Vessel 53.52311 8.58042
1 2023-05-08 13:08:00 8.58042 53.52311 Heincke Vessel 53.52311 8.58042
2 2023-05-08 13:18:00 8.58042 53.52311 Heincke Vessel 53.52311 8.58042
3 2023-05-08 13:28:00 8.58043 53.52311 Heincke Vessel 53.52311 8.58043
4 2023-05-08 13:38:00 8.58042 53.52311 Heincke Vessel 53.52311 8.58042
... ... ... ... ... ... ... ...
67 2023-05-09 09:08:00 8.58042 53.52310 Heincke Vessel 53.52310 8.58042
68 2023-05-09 09:18:00 8.58042 53.52310 Heincke Vessel 53.52310 8.58042
69 2023-05-09 09:28:00 8.58042 53.52310 Heincke Vessel 53.52310 8.58042
70 2023-05-09 09:38:00 8.58042 53.52310 Heincke Vessel 53.52310 8.58042
71 2023-05-09 09:48:00 8.58042 53.52310 Heincke Vessel 53.52310 8.58042

72 rows × 7 columns

Plot¶

In [6]:
hover = HoverTool(tooltips=[
    ("Platform", "@Platform"),
    ("Type", "@Type"),
    ("Timestamp", "@Timestamp"),
    ("Longitude", "@Longitude_"),  # display potentially redacted positions
    ("Latitude", "@Latitude_"),  # display potentially redacted positions
])
In [7]:
savetool = SaveTool()
In [8]:
plots = []

for npf, pf in enumerate(df.Platform.unique()):
    if npf == 0:
        _kwargs = dict(
            tiles="OSM",  
            xlim=(9.8, 12.3),
            ylim=(54.2, 55),
        )
    else:
        _kwargs = {}
    _df = df.where(
        (df.Platform == pf)
        & (df.Timestamp > platform_deploy_times[pf])
    ).dropna().sort_values("Timestamp")
    _df["Timestamp"] = _df["Timestamp"].astype(str)  # needed for proper display
    plots.append(
        (
            _df.hvplot.paths(
                x="Longitude", y="Latitude", line_width=2,
                label=pf,
                geo=True,
                **_kwargs
            )
            * _df.hvplot.points(
                x="Longitude", y="Latitude", size=25, marker="x",
                label=pf,
                hover_cols=["Platform", "Type", "Longitude_", "Latitude_", "Timestamp", ], tools=[hover, ],
                geo=True,
            )
        )
    )
In [9]:
plots.append(
    (
        df_heincke.hvplot.paths(
            x="Longitude", y="Latitude", size=95, color="magenta", geo=True, label="Heincke", hover_cols=["Timestamp", ] 
        )
        * df_heincke.iloc[-1:].hvplot.points(
            x="Longitude", y="Latitude", size=95, color="magenta", marker=">", geo=True, label="Heincke", hover_cols=["Timestamp", ] 
        )
    )
)
In [10]:
plot = reduce(lambda a, b: a * b, plots[1:], plots[0]).opts(frame_width=700)

Display¶

In [11]:
now = !date -Is -u
now = now[0]
In [12]:
display(plot.opts(title=f"{now} UTC"));

Last 3 positions¶

In [13]:
pd.concat((df, df_heincke), ignore_index=True).groupby("Platform").tail(3).set_index(["Platform", "Timestamp"])[["Longitude_", "Latitude_"]]
Out[13]:
Longitude_ Latitude_
Platform Timestamp
Littorina 2023-05-09 06:55:06+00:00 10.90000 56.40000
2023-05-09 07:58:05+00:00 10.90000 56.40000
2023-05-09 08:55:06+00:00 10.90000 56.40000
D298 2023-05-05 16:11:22+00:00 11.13679 54.48502
2023-05-05 16:16:24+00:00 11.13685 54.48501
2023-05-05 16:22:16+00:00 11.13705 54.48495
D299 2023-05-06 16:01:21+00:00 10.13019 54.35603
2023-05-06 16:06:18+00:00 10.13015 54.35589
2023-05-06 16:21:16+00:00 10.14892 54.33151
D300 2023-05-05 13:32:04+00:00 11.07306 54.53201
2023-05-05 13:36:27+00:00 11.07310 54.53198
2023-05-05 13:51:27+00:00 11.07315 54.53195
D301 2023-05-05 06:48:59+00:00 10.16894 54.33173
2023-05-05 06:54:51+00:00 10.15296 54.33286
2023-05-05 06:58:58+00:00 10.15124 54.33219
D302 2023-05-06 16:21:24+00:00 10.14849 54.33080
2023-05-06 19:36:23+00:00 10.14763 54.32993
2023-05-06 21:52:31+00:00 10.14643 54.32990
D303 2023-05-09 09:20:47+00:00 10.73313 54.93616
2023-05-09 09:25:48+00:00 10.73306 54.93605
2023-05-09 09:40:44+00:00 10.73317 54.93613
Heincke 2023-05-09 09:28:00 8.58042 53.52310
2023-05-09 09:38:00 8.58042 53.52310
2023-05-09 09:48:00 8.58042 53.52310
In [14]:
def generate_google_maps_link(Longitude, Latitude):
    return f"https://www.google.com/maps/place/{Latitude}+{Longitude}"
In [15]:
links = (
    pd.concat((df, df_heincke), ignore_index=True)
    .groupby("Platform").tail(1)
    .set_index(["Platform", "Timestamp"])
    [["Longitude", "Latitude"]]
    .apply(lambda r: generate_google_maps_link(**r), axis=1)
)
In [16]:
for r in links.items():
    print(f"{r[0][0]} @ {r[0][1]}\n\t{r[1]}")

Littorina @ 2023-05-09 08:55:06+00:00
	https://www.google.com/maps/place/56.41609+10.92528
D298 @ 2023-05-05 16:22:16+00:00
	https://www.google.com/maps/place/54.48495+11.13705
D299 @ 2023-05-06 16:21:16+00:00
	https://www.google.com/maps/place/54.33151+10.14892
D300 @ 2023-05-05 13:51:27+00:00
	https://www.google.com/maps/place/54.53195+11.07315
D301 @ 2023-05-05 06:58:58+00:00
	https://www.google.com/maps/place/54.33219+10.15124
D302 @ 2023-05-06 21:52:31+00:00
	https://www.google.com/maps/place/54.3299+10.14643
D303 @ 2023-05-09 09:40:44+00:00
	https://www.google.com/maps/place/54.93613+10.73317
Heincke @ 2023-05-09 09:48:00
	https://www.google.com/maps/place/53.5231+8.58042
In [17]:
!date -Is -u

2023-05-09T09:57:36+00:00